Fire Detection System Using Optical Flow and Image Feature Analysis
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Solution Overview
Problem
Existing fire detection systems struggle to quickly and accurately detect fires in their initial stages, leading to potential loss of life, property damage, and environmental harm.
Innovation Solution
A fire detecting system utilizing deep learning techniques, including an optical flow detecting module, image feature detecting module, candidate area analyzing module, ignition point estimating module, fire classifying module, and temporal analysis module, to analyze input images and determine the presence and classification of fires.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fire detection methods are used, then the system is simple to implement, but the detection accuracy and speed in initial fire stages are insufficient
Solution Approach 1:
The fire detection system is divided into multiple specialized modules: optical flow detection module for motion analysis, image feature detection module for visual characteristics, candidate area analysis module for region identification, ignition point estimation module for source localization, fire classification module for type identification, and temporal analysis module for time-based verification. Each module focuses on specific detection aspects, improving overall accuracy while managing complexity through functional decomposition
Solution Approach 2:
A candidate area analysis module serves as an intermediary between initial detection modules and final classification modules. It processes output from optical flow and image feature detectors, identifies suspicious regions, and passes refined information to subsequent modules. This intermediary layer improves detection accuracy by focusing computational resources on relevant areas while managing system complexity through staged processing
2Measurement precision
If deep learning models are trained with extensive training data, then detection accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training deep learning models with optical flow training data and generated fire training data before actual fire detection operations. The optical flow model is trained with synthetic optical flow data, and the fire detection model is trained with computer-generated fire images. This preliminary training prepares the models for accurate real-time detection, reducing the need for extensive training during deployment and improving detection accuracy
Solution Approach 2:
The system uses computer graphic processes to generate synthetic training data that copies real fire characteristics. Generated fire training data and synthetic optical flow training data are created to simulate various fire scenarios, flames, and smoke patterns. This copying approach provides extensive training data without requiring proportional amounts of real fire footage, reducing training time while maintaining detection accuracy
3Reliability
If the system processes multiple image frames with temporal analysis, then false detections are reduced, but processing speed decreases
Solution Approach 1:
The temporal analysis module applies partial action by performing temporal coherence analysis selectively on candidate areas identified by previous modules rather than processing entire frames. It verifies suspicious regions across multiple time points only when initial detection thresholds are met. This approach maintains high detection reliability by confirming fires through temporal consistency while preserving processing speed by avoiding full-frame temporal analysis in all cases
Data Source
AI summary
Disclosed is a fire detecting system. The fire detecting system includes: an optical flow detecting module for estimating a motion of a fire element from an input image, and determining a first candidate area in the input image; an image feature detecting module for recognizing an image feature of the fire element from the input image, and determining a second candidate area in the input image; a candidate area analyzing module for determining a suspicion area in which a generation of fire is suspected in the input image based on the first candidate area and the second candidate area; an ignition point estimating module for estimating a position of an ignition point in the suspicion area; a fire classifying module for calculating classifying scores obtained by predicting whether a fire is generated in the suspicion area; and a temporal analysis module for determining whether a fire is generated based on the position of the ignition point and the classifying scores.